Class factorized complex variational auto-encoder for HRR radar target recognition
•A class factorized complex variational auto-encoder is proposed and used for RATR.•Our model is a complex-valued neural network for utilizing the phase of HRR data.•Our method adopts class-decoders to depict the generation of data from each class.•The learned prior of the latent variable helps lear...
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| Veröffentlicht in: | Signal processing Jg. 182; S. 107932 |
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| Hauptverfasser: | , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Elsevier B.V
01.05.2021
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| Schlagworte: | |
| ISSN: | 0165-1684, 1872-7557 |
| Online-Zugang: | Volltext |
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| Zusammenfassung: | •A class factorized complex variational auto-encoder is proposed and used for RATR.•Our model is a complex-valued neural network for utilizing the phase of HRR data.•Our method adopts class-decoders to depict the generation of data from each class.•The learned prior of the latent variable helps learn the discriminative features.•Experiments on measured HRR data show our method outperforms other related methods.
In the field of radar automatic target recognition (RATR), the high-resolution range profile (HRRP) has received intensive attention. Bar a few exceptions, almost all HRRP-based ATR classification systems ignore the phase of the HRRP when the data is input to the classifier, relying instead only on the magnitude of the complex HRRP samples. This approach ignores the phase of the complex HRRPs, which reduces the information in the signal. In this paper, we develop a novel class factorized complex variational auto-encoder (CFCVAE) to utilize the phase of the high range resolution (HRR) radar echo for recognition. The CFCVAE is a complex-valued neural network (CV-NN) consisting of the encoding and decoding modules. In CFCVAE, the encoding module projects the observed data into the latent space, and then the latent features are fed to the decoding module, which further maps the latent features to data. In particular, the decoding module introduces the class labels to partition the whole observations into some parts, each of which is depicted by a specific class-decoder. Compared with the traditional variational auto-encoder (VAE) containing a single decoder, the CFCVAE can give a more accurate description to the whole dataset via multiple class-decoders, thus improving the characterization ability of features. In addition, based on the class labels, the CFCVAE employs the conditional prior on the latent variable to enhance the discrimination of features. Moreover, a complex backpropagation algorithm is derived for CFCVAE training, and a sample is classified to the class corresponding to the class-decoder with the minimum reconstruction error in the test stage. Experimental evaluations on the measured data indicate that the proposed method indeed achieves very promising target recognition performance. |
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| ISSN: | 0165-1684 1872-7557 |
| DOI: | 10.1016/j.sigpro.2020.107932 |